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The authors contrast Beckett and Professional Sports Authenticator (PSA) baseball card valuations. Also, the authors contrast the Bill James statistics for winshares (WIN) and reference.com statistics for wins above replacement (WAR).

This study examines the impact of analytics on Topps 1957 baseball card values.

The authors' examination of variables that influence Topps 1957 baseball card values yields similar results for mint and very good rated cards over the early period (1982), pre-strike (1989), post-strike (1998) and recent (2009) periods. In single variable and multiple regressions, Baseball Hall of Fame (HOF) membership and New York Yankee (NYY) nostalgia coefficient are significant at the 5% level or higher for mint and very good rated cards over all reported periods. The Brooklyn Dodger (BD) parameter is significant at the 5% level or higher in single variable regressions for all reported periods and for 1982 and 1989 for multiple regressions. Reflecting a lack of nostalgia, the New York Giant card coefficients are statistically insignificant over all periods. Also, the authors see a lack of negative bias for Black-player cards. The authors observe that Black-player card coefficients are positive and sometimes statistically significant. This indicates a positive relationship between Black-player cards and prices.

This is the first study to examine the impact of WINS and WAR analytics on baseball card values.

This study aims to model tourist activities in a network and explore the properties of the network. Such network enables the author to explain and quantify how tourist activities are connected in determining tourist consumption as well as the organization of destination supply.

The author developed a network formation mechanism to create edges between nodes based on the joint probability of a pair of activities undertaken by tourists at a destination. By adjusting network sparsity, the author created an ensemble of four topologically similar networks for empirical testing. The author used tourist activity data of Hong Kong inbound tourists to test the network model.

The author found a robust hub–periphery topological structure of the tourist activity network. In addition, the network is featured by high clustering, short diameter and positive correlations between four node centralities, namely, degree, closeness, betweenness and eigenvector centralities. The author also generated the k-cores of the networks to further unravel the structure of hub nodes. The author found that the k-cores are dominated by tourist activities related to shopping or sightseeing, suggesting the high complementarity of these activities.

This study provides a different lens through which tourist consumption can be understood from a macroscopic angle by examining network topology and from a microscopic angle by examining node centralities.

To the best of the author’s knowledge, this is the first study attempting to model tourist activity and consumption in a network and explore the properties of the network. Not only has this study provided a new real-world network for network research, but it has also suggested an innovative modeling approach for tourist behavior research.